One-Year Effects of High-Intensity Statin on Bioactive Lipids: Findings From the JUPITER Trial.

BACKGROUND: Statin effects extend beyond low-density lipoprotein cholesterol reduction, potentially modulating the metabolism of bioactive lipids (BALs), crucial for biological signaling and inflammation. These bioactive metabolites may serve as metabolic footprints, helping uncover underlying processes linked to pleiotropic effects of statins and yielding a better understanding of their cardioprotective properties. This study aimed to investigate the impact of high-intensity statin therapy versus placebo on plasma BALs in the JUPITER trial (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; NCT00239681), a randomized primary prevention trial involving individuals with low-density lipoprotein cholesterol <130 mg/dL and high-sensitivity C-reactive protein ≥2 mg/L. METHODS: Using a nontargeted mass spectrometry approach, over 11 000 lipid features were assayed from baseline and 1-year plasma samples from cardiovascular disease noncases from 2 nonoverlapping nested substudies: JUPITERdiscovery (n=589) and JUPITERvalidation (n=409). The effect of randomized allocation of rosuvastatin 20 mg versus placebo on BALs was examined by fitting a linear regression with delta values (∆=year 1-baseline) adjusted for age and baseline levels of each feature. Significant associations in discovery were analyzed in the validation cohort. Multiple comparisons were adjusted using 2-stage overall false discovery rate. RESULTS: We identified 610 lipid features associated with statin randomization with significant replication (overall false discovery rate, <0.05), including 26 with annotations. Statin therapy significantly increased levels of 276 features, including BALs with anti-inflammatory activity and arterial vasodilation properties. Concurrently, 334 features were significantly lowered by statin therapy, including arachidonic acid and proinflammatory and proplatelet aggregation BALs. By contrast, statin therapy reduced an eicosapentaenoic acid-derived hydroxyeicosapentaenoic acid metabolite, which may be related to impaired glucose metabolism. Additionally, we observed sex-related differences in 6 lipid metabolites and 6 unknown features. CONCLUSIONS: Statin allocation was significantly associated with upregulation of BALs with anti-inflammatory, antiplatelet aggregation and antioxidant properties and downregulation of BALs with proinflammatory and proplatelet aggregation activity, supporting the pleiotropic effects of statins beyond low-density lipoprotein cholesterol reduction.

Mediterranean Diet Adherence and Risk of All-Cause Mortality in Women.

Importance: Higher adherence to the Mediterranean diet has been associated with reduced risk of all-cause mortality, but data on underlying molecular mechanisms over long follow-up are limited. Objectives: To investigate Mediterranean diet adherence and risk of all-cause mortality and to examine the relative contribution of cardiometabolic factors to this risk reduction. Design, Setting, and Participants: This cohort study included initially healthy women from the Women's Health Study, who had provided blood samples, biomarker measurements, and dietary information. Baseline data included self-reported demographics and a validated food-frequency questionnaire. The data collection period was from April 1993 to January 1996, and data analysis took place from June 2018 to November 2023. Exposures: Mediterranean diet score (range, 0-9) was computed based on 9 dietary components. Main Outcome and Measures: Thirty-three blood biomarkers, including traditional and novel lipid, lipoprotein, apolipoprotein, inflammation, insulin resistance, and metabolism measurements, were evaluated at baseline using standard assays and nuclear magnetic resonance spectroscopy. Mortality and cause of death were determined from medical and death records. Cox proportional hazards regression was used to calculate hazard ratios (HRs) for Mediterranean diet adherence and mortality risk, and mediation analyses were used to calculate the mediated effect of different biomarkers in understanding this association. Results: Among 25 315 participants, the mean (SD) baseline age was 54.6 (7.1) years, with 329 (1.3%) Asian women, 406 (1.6%) Black women, 240 (0.9%) Hispanic women, 24 036 (94.9%) White women, and 95 (0.4%) women with other race and ethnicity; the median (IQR) Mediterranean diet adherence score was 4.0 (3.0-5.0). Over a mean (SD) of 24.7 (4.8) years of follow-up, 3879 deaths occurred. Compared with low Mediterranean diet adherence (score 0-3), adjusted risk reductions were observed for middle (score 4-5) and upper (score 6-9) groups, with HRs of 0.84 (95% CI, 0.78-0.90) and 0.77 (95% CI, 0.70-0.84), respectively (P for trend < .001). Further adjusting for lifestyle factors attenuated the risk reductions, but they remained statistically significant (middle adherence group: HR, 0.92 [95% CI, 0.85-0.99]; upper adherence group: HR, 0.89 [95% CI, 0.82-0.98]; P for trend = .001). Of the biomarkers examined, small molecule metabolites and inflammatory biomarkers contributed most to the lower mortality risk (explaining 14.8% and 13.0%, respectively, of the association), followed by triglyceride-rich lipoproteins (10.2%), body mass index (10.2%), and insulin resistance (7.4%). Other pathways, including branched-chain amino acids, high-density lipoproteins, low-density lipoproteins, glycemic measures, and hypertension, had smaller contributions (<3%). Conclusions and Relevance: In this cohort study, higher adherence to the Mediterranean diet was associated with 23% lower risk of all-cause mortality. This inverse association was partially explained by multiple cardiometabolic factors.

Plasma Biomarker Profiles for Premature and Nonpremature Coronary Heart Disease in Women.

BACKGROUND: Premature coronary heart disease (CHD) is a major cause of death in women. We aimed to characterize biomarker profiles of women who developed CHD before and after age 65 years. METHODS: In the Women's Health Study (median follow-up 21.5 years), women were grouped by age and timing of incident CHD: baseline age <65 years with premature CHD by age 65 years (25 042 women; 447 events) and baseline age ≥65 years with nonpremature CHD (2982 women; 351 events). Associations of 44 baseline plasma biomarkers measured using standard assays and a nuclear magnetic resonance (NMR)-metabolomics assay were analyzed using Cox models adjusted for clinical risk factors. RESULTS: Twelve biomarkers showed associations only with premature CHD and included lipoprotein(a), which was associated with premature CHD [adjusted hazard ratio (HR) per SD: 1.29 (95% CI 1.17-1.42)] but not with nonpremature CHD [1.09(0.98-1.22)](Pinteraction = 0.02). NMR-measured lipoprotein insulin resistance was associated with the highest risk of premature CHD [1.92 (1.52-2.42)] but was not associated with nonpremature CHD (Pinteraction <0.001). Eleven biomarkers showed stronger associations with premature vs nonpremature CHD, including apolipoprotein B. Nine NMR biomarkers showed no association with premature or nonpremature CHD, whereas 12 biomarkers showed similar significant associations with premature and nonpremature CHD, respectively, including low-density lipoprotein (LDL) cholesterol [1.30(1.20-1.45) and 1.22(1.10-1.35)] and C-reactive protein [1.34(1.19-1.50) and 1.25(1.08-1.44)]. CONCLUSIONS: In women, a profile of 12 biomarkers was selectively associated with premature CHD, driven by lipoprotein(a) and insulin-resistant atherogenic dyslipoproteinemia. This has implications for the development of biomarker panels to screen for premature CHD.

N-Glycosylation Profiles of Immunoglobulin G and Future Cardiovascular Events.

BACKGROUND: Posttranslational glycosylation of IgG can modulate its inflammatory capacity through structural variations. We examined the association of baseline IgG N-glycans and an IgG glycan score with incident cardiovascular disease (CVD). METHODS: IgG N-glycans were measured in 2 nested CVD case-control studies: JUPITER (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; NCT00239681; primary prevention; discovery; Npairs=162); and TNT trial (Treating to New Targets; NCT00327691; secondary prevention; validation; Npairs=397). Using conditional logistic regression, we investigated the association of future CVD with baseline IgG N-glycans and a glycan score adjusting for clinical risk factors (statin treatment, age, sex, race, lipids, hypertension, and smoking) in JUPITER. Significant associations were validated in TNT, using a similar model further adjusted for diabetes. Using least absolute shrinkage and selection operator regression, an IgG glycan score was derived in JUPITER as a linear combination of selected IgG N-glycans. RESULTS: Six IgG N-glycans were associated with CVD in both studies: an agalactosylated glycan (IgG-GP4) was positively associated, while 3 digalactosylated glycans (IgG glycan peaks 12, 13, 14) and 2 monosialylated glycans (IgG glycan peaks 18, 20) were negatively associated with CVD after multiple testing correction (overall false discovery rate <0.05). Four selected IgG N-glycans comprised the IgG glycan score, which was associated with CVD in JUPITER (adjusted hazard ratio per glycan score SD, 2.08 [95% CI, 1.52-2.84]) and validated in TNT (adjusted hazard ratio per SD, 1.20 [95% CI, 1.03-1.39]). The area under the curve changed from 0.693 for the model without the score to 0.728 with the score in JUPITER (PLRT=1.1×10-6) and from 0.635 to 0.637 in TNT (PLRT=0.017). CONCLUSIONS: An IgG N-glycan profile was associated with incident CVD in 2 populations (primary and secondary prevention), involving an agalactosylated glycan associated with increased risk of CVD, while several digalactosylated and sialylated IgG glycans associated with decreased risk. An IgG glycan score was positively associated with future CVD.

The Mediterranean Diet, Cardiometabolic Biomarkers, and Risk of All-Cause Mortality: A 25-Year Follow-Up Study of the Women's Health Study.

Background: Higher consumption of Mediterranean diet (MED) intake has been associated with reduced risk of all-cause mortality but limited data are available examining long-term outcomes in women or the underlying molecular mechanisms of this inverse association in human populations. We aimed to investigate the association of MED intake with long-term risk of all-cause mortality in women and to better characterize the relative contribution of traditional and novel cardiometabolic factors to the MED-related risk reduction in morality. Methods: In a prospective cohort study of 25,315 initially healthy women from the Women's Health Study, we assessed dietary MED intake using a validated semiquantitative food frequency questionnaire according to the usual 9-category measure of MED adherence. Baseline levels of more than thirty cardiometabolic biomarkers were measured using standard assays and targeted nuclear magnetic resonance spectroscopy, including lipids, lipoproteins, apolipoproteins, inflammation, glucose metabolism and insulin resistance, branched-chain amino acids, small metabolites, and clinical factors. Mortality and cause of death was ascertained prospectively through medical and death records. Results: During a mean follow-up of 25 years, 3,879 deaths were ascertained. Compared to the reference group of low MED intake (0-3, approximately the bottom tertile), and adjusting for age, treatment, and energy intake, risk reductions were observed for the middle and upper MED groups with respective HRs of 0.84 (95% CI 0.78-0.90) and 0.77 (95% CI 0.70-0.84), p for trend <0.0001. Further adjusting for smoking, physical activity, alcohol intake and menopausal factors attenuated the risk reductions which remained significant with respective HRs of 0.92 (95% CI 0.85-0.99) and 0.89 (95% CI 0.82-0.98), p for trend 0.0011. Risk reductions were generally similar for CVD and non-CVD mortality. Small molecule metabolites (e.g., alanine and homocysteine) and inflammation made the largest contributions to lower mortality risk (accounting for 14.8% and 13.0% of the benefit of the MED-mortality association, respectively), followed by triglyceride-rich lipoproteins (10.2%), adiposity (10.2%) and insulin resistance (7.4%), with lesser contributions (<3%) from other pathways including branched-chain amino acids, high-density lipoproteins, low-density lipoproteins, glycemic measures, and hypertension. Conclusions: In the large-scale prospective Women's Health Study of 25,315 initially healthy US women followed for 25 years, higher MED intake was associated with approximately one fifth relative risk reduction in mortality. The inverse association was only partially explained by known novel and traditional cardiometabolic factors.

Age of onset and cumulative risk of mental disorders: a cross-national analysis of population surveys from 29 countries.

BACKGROUND: Information on the frequency and timing of mental disorder onsets across the lifespan is of fundamental importance for public health planning. Broad, cross-national estimates of this information from coordinated general population surveys were last updated in 2007. We aimed to provide updated and improved estimates of age-of-onset distributions, lifetime prevalence, and morbid risk. METHODS: In this cross-national analysis, we analysed data from respondents aged 18 years or older to the World Mental Health surveys, a coordinated series of cross-sectional, face-to-face community epidemiological surveys administered between 2001 and 2022. In the surveys, the WHO Composite International Diagnostic Interview, a fully structured psychiatric diagnostic interview, was used to assess age of onset, lifetime prevalence, and morbid risk of 13 DSM-IV mental disorders until age 75 years across surveys by sex. We did not assess ethnicity. The surveys were geographically clustered and weighted to adjust for selection probability, and standard errors of incidence rates and cumulative incidence curves were calculated using the jackknife repeated replications simulation method, taking weighting and geographical clustering of data into account. FINDINGS: We included 156 331 respondents from 32 surveys in 29 countries, including 12 low-income and middle-income countries and 17 high-income countries, and including 85 308 (54·5%) female respondents and 71 023 (45·4%) male respondents. The lifetime prevalence of any mental disorder was 28·6% (95% CI 27·9-29·2) for male respondents and 29·8% (29·2-30·3) for female respondents. Morbid risk of any mental disorder by age 75 years was 46·4% (44·9-47·8) for male respondents and 53·1% (51·9-54·3) for female respondents. Conditional probabilities of first onset peaked at approximately age 15 years, with a median age of onset of 19 years (IQR 14-32) for male respondents and 20 years (12-36) for female respondents. The two most prevalent disorders were alcohol use disorder and major depressive disorder for male respondents and major depressive disorder and specific phobia for female respondents. INTERPRETATION: By age 75 years, approximately half the population can expect to develop one or more of the 13 mental disorders considered in this Article. These disorders typically first emerge in childhood, adolescence, or young adulthood. Services should have the capacity to detect and treat common mental disorders promptly and to optimise care that suits people at these crucial parts of the life course. FUNDING: None.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Association of Physical Activity With Bioactive Lipids and Cardiovascular Events.

BACKGROUND: To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS: PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS: We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS: We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data.

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.

Association of Lipid, Inflammatory, and Metabolic Biomarkers With Age at Onset for Incident Coronary Heart Disease in Women.

Importance: Risk profiles for premature coronary heart disease (CHD) are unclear. Objective: To examine baseline risk profiles for incident CHD in women by age at onset. Design, Setting, and Participants: A prospective cohort of US female health professionals participating in the Women's Health Study was conducted; median follow-up was 21.4 years. Participants included 28 024 women aged 45 years or older without known cardiovascular disease. Baseline profiles were obtained from April 30, 1993, to January 24, 1996, and analyses were conducted from October 1, 2017, to October 1, 2020. Exposures: More than 50 clinical, lipid, inflammatory, and metabolic risk factors and biomarkers. Main Outcomes and Measures: Four age groups were examined (<55, 55 to <65, 65 to <75, and ≥75 years) for CHD onset, and adjusted hazard ratios (aHRs) were calculated using stratified Cox proportional hazard regression models with age as the time scale and adjusting for clinical factors. Women contributed to different age groups over time. Results: Of the clinical factors in the women, diabetes had the highest aHR for CHD onset at any age, ranging from 10.71 (95% CI, 5.57-20.60) at CHD onset in those younger than 55 years to 3.47 (95% CI, 2.47-4.87) at CHD onset in those 75 years or older. Risks that were also noted for CHD onset in participants younger than 55 years included metabolic syndrome (aHR, 6.09; 95% CI, 3.60-10.29), hypertension (aHR, 4.58; 95% CI, 2.76-7.60), obesity (aHR, 4.33; 95% CI, 2.31-8.11), and smoking (aHR, 3.92; 95% CI, 2.32-6.63). Myocardial infarction in a parent before age 60 years was associated with 1.5- to 2-fold risk of CHD in participants up to age 75 years. From approximately 50 biomarkers, lipoprotein insulin resistance had the highest standardized aHR: 6.40 (95% CI, 3.14-13.06) for CHD onset in women younger than 55 years, attenuating with age. In comparison, weaker but significant associations with CHD in women younger than 55 years were noted (per SD increment) for low-density lipoprotein cholesterol (aHR, 1.38; 95% CI, 1.10-1.74), non-high-density lipoprotein cholesterol (aHR, 1.67; 95% CI, 1.36-2.04), apolipoprotein B (aHR, 1.89; 95% CI, 1.52-2.35), triglycerides (aHR, 2.14; 95% CI, 1.72-2.67), and inflammatory biomarkers (1.2- to 1.8-fold)-all attenuating with age. Some biomarkers had similar CHD age associations (eg, physical inactivity, lipoprotein[a], total high-density lipoprotein particles), while a few had no association with CHD onset at any age. Most risk factors and biomarkers had associations that attenuated with increasing age at onset. Conclusions and Relevance: In this cohort study, diabetes and insulin resistance, in addition to hypertension, obesity, and smoking, appeared to be the strongest risk factors for premature onset of CHD. Most risk factors had attenuated relative rates at older ages.

Association of the Mediterranean Diet With Onset of Diabetes in the Women's Health Study.

Importance: Higher Mediterranean diet (MED) intake has been associated with reduced risk of type 2 diabetes, but underlying biological mechanisms are unclear. Objective: To characterize the relative contribution of conventional and novel biomarkers in MED-associated type 2 diabetes risk reduction in a US population. Design, Setting, and Participants: This cohort study was conducted among 25 317 apparently healthy women. The participants with missing information regarding all traditional and novel metabolic biomarkers or those with baseline diabetes were excluded. Participants were invited for baseline assessment between September 1992 and May 1995. Data were collected from November 1992 to December 2017 and analyzed from December 2018 to December 2019. Exposures: MED intake score (range, 0 to 9) was computed from self-reported dietary intake, representing adherence to Mediterranean diet intake. Main Outcomes and Measures: Incident cases of type 2 diabetes, identified through annual questionnaires; reported cases were confirmed by either telephone interview or supplemental questionnaire. Proportion of reduced risk of type 2 diabetes explained by clinical risk factors and a panel of 40 biomarkers that represent different physiological pathways was estimated. Results: The mean (SD) age of the 25 317 female participants was 52.9 (9.9) years, and they were followed up for a mean (SD) of 19.8 (5.8) years. Higher baseline MED intake (score ≥6 vs ≤3) was associated with as much as a 30% lower type 2 diabetes risk (age-adjusted and energy-adjusted hazard ratio, 0.70; 95% CI, 0.62-0.79; when regression models were additionally adjusted with body mass index [BMI]: hazard ratio, 0.85; 95% CI, 0.76-0.96). Biomarkers of insulin resistance made the largest contribution to lower risk (accounting for 65.5% of the MED-type 2 diabetes association), followed by BMI (55.5%), high-density lipoprotein measures (53.0%), and inflammation (52.5%), with lesser contributions from branched-chain amino acids (34.5%), very low-density lipoprotein measures (32.0%), low-density lipoprotein measures (31.0%), blood pressure (29.0%), and apolipoproteins (23.5%), and minimal contribution (≤2%) from hemoglobin A1c. In post hoc subgroup analyses, the inverse association of MED diet with type 2 diabetes was seen only among women who had BMI of at least 25 at baseline but not those who had BMI of less than 25 (eg, women with BMI <25, age- and energy-adjusted HR for MED score ≥6 vs ≤3, 1.01; 95% CI, 0.77-1.33; P for trend = .92; women with BMI ≥25: HR, 0.76; 95% CI, 0.67-0.87; P for trend < .001). Conclusions and Relevance: In this cohort study, higher MED intake scores were associated with a 30% relative risk reduction in type 2 diabetes during a 20-year period, which could be explained in large part by biomarkers of insulin resistance, BMI, lipoprotein metabolism, and inflammation.

One-Year Effects of Omega-3 Treatment on Fatty Acids, Oxylipins, and Related Bioactive Lipids and Their Associations with Clinical Lipid and Inflammatory Biomarkers: Findings from a Substudy of the Vitamin D and Omega-3 Trial (VITAL).

Omega-3 (n-3) treatment may lower cardiovascular risk, yet its effects on the circulating lipidome and relation to cardiovascular risk biomarkers are unclear. We hypothesized that n-3 treatment is associated with favorable changes in downstream fatty acids (FAs), oxylipins, bioactive lipids, clinical lipid and inflammatory biomarkers. We examined these VITAL200, a nested substudy of 200 subjects balanced on demographics and treatment and randomly selected from the Vitamin D and Omega-3 Trial (VITAL). VITAL is a randomized double-blind trial of 840 mg/d eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) vs. placebo among 25,871 individuals. Small polar bioactive lipid features, oxylipins and FAs from plasma and red blood cells were measured using three independent assaying techniques at baseline and one year. The Women's Health Study (WHS) was used for replication with dietary n-3 intake. Randomized n-3 treatment led to changes in 143 FAs, oxylipins and bioactive lipids (False Discovery Rate (FDR) < 0.05 in VITAL200, validated (p-values < 0.05)) in WHS with increases in 95 including EPA, DHA, n-3 docosapentaenoic acid (DPA-n3), and decreases in 48 including DPA-n6, dihomo gamma linolenic (DGLA), adrenic and arachidonic acids. N-3 related changes in the bioactive lipidome were heterogeneously associated with changes in clinical lipid and inflammatory biomarkers. N-3 treatment significantly modulates the bioactive lipidome, which may contribute to its clinical benefits.

Anti-Inflammatory HDL Function, Incident Cardiovascular Events, and Mortality: A Secondary Analysis of the JUPITER Randomized Clinical Trial.

Background High-density lipoprotein (HDL) cholesterol has inverse association with cardiovascular disease. HDL possesses anti-inflammatory properties in vitro, but it is unknown whether this may be protective in individuals with inflammation. Methods and Results The functional capacity of HDL to inhibit oxidation of oxidized low-density lipoprotein (ie, the HDL inflammatory index; HII) was measured at baseline and 12 months after random allocation to rosuvastatin or placebo in a nested case-control study of the JUPITER (Justification for the Use of Statins in Prevention: An Intervention Evaluating Rosuvastatin) trial. There were 517 incident cases of cardiovascular disease and all-cause mortality compared to 517 age- and sex-matched controls. Multivariable conditional logistic regression was used to examine associations of HII with events. Median baseline HII was 0.54 (interquartile range, 0.50-0.59). Twelve months of rosuvastatin decreased HII by a mean of 5.3% (95% CI, -8.9% to -1.7%; P=0.005) versus 1.3% (95% CI, -6.5% to 4.0%; P=0.63) with placebo (P=0.22 for between-group difference). HII had a nonlinear relationship with incident events. Compared with the reference group (HII 0.5-1.0) with the lowest event rates, participants with baseline HII ≤0.5 had significantly increased risk of cardiovascular disease/mortality (adjusted hazard ratio, 1.53; 95% CI, 1.06-2.21; P=0.02). Furthermore, there was significant (P=0.002) interaction for HDL particle number with HII, such that having more HDL particles was associated with decreased risk only when HDL was anti-inflammatory. Conclusions In JUPITER participants recruited on the basis of chronic inflammation, HII was associated with incident cardiovascular disease/mortality, with an optimal anti-inflammatory HII range between 0.5 and 1.0. This nonlinear relationship of anti-inflammatory HDL function with risk may account in part for the HDL paradox. Registration URL: https://www.clini​caltr​ials.gov; Unique identifier: NCT00239681.

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes.

The chemical composition of saccharide complexes underlies their biomedical activities as biomarkers for cardiometabolic disease, various types of cancer, and other conditions. However, because these molecules may undergo major structural modifications, distinguishing between compounds of saccharide and non-saccharide origin becomes a challenging computational problem that hinders the aggregation of information about their bioactive moieties. We have developed an algorithm and software package called "Cheminformatics Tool for Probabilistic Identification of Carbohydrates" (CTPIC) that analyzes the covalent structure of a compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharides. CTPIC analysis of the RCSB Ligand Expo (database of small molecules found to bind proteins in the Protein Data Bank) led to a substantial increase in the number of ligands characterized as saccharides. CTPIC analysis of Protein Data Bank identified 7.7% of the proteins as saccharide-binding. CTPIC is freely available as a webservice at (http://ctpic.nmrfam.wisc.edu).

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

Pleiotropy-Based Decomposition of Genetic Risk Scores: Association and Interaction Analysis for Type 2 Diabetes and CAD.

Genetic risk for a disease in the population may be represented as a genetic risk score (GRS) constructed as the sum of inherited risk alleles, weighted by allelic effects established in an independent population. While this formulation captures overall genetic risk, it typically does not address risk due to specific biological mechanisms or pathways that may nevertheless be important for interpretation or treatment response. Here, a GRS for disease is resolved into independent or nearly independent components pertaining to biological mechanisms inferred from pleiotropic relationships. The component GRSs' weights are derived from the singular value decomposition (SVD) of the matrix of appropriately scaled genetic effects, i.e., beta coefficients, of the disease variants across a panel of the disease-related phenotypes. The SVD-based formalism also associates combinations of disease-related phenotypes with inferred disease pathways. Applied to incident type 2 diabetes (T2D) in the Women's Genome Health Study (N = 23,294), component GRSs discriminate glycemic control and lipid-based genetic risk, while revealing significant interactions between specific components and BMI or physical activity, the latter not observed with a GRS for overall T2D genetic liability. Applied to coronary artery disease (CAD) in both the WGHS and in JUPITER (N = 8,749), a randomized trial of rosuvastatin for primary prevention of CVD, component GRSs discriminate genetic risk associated with LDL-C from risk associated with reciprocal genetic effects on triglycerides and HDL-C. They also inform the pharmacogenetics of statin treatment by demonstrating that benefit from rosuvastatin is as strongly related to genetic risk from triglycerides and HDL-C as from LDL-C.

Comparison of nonfasting and fasting lipoprotein subfractions and size in 15,397 apparently healthy individuals: An analysis from the VITamin D and OmegA-3 TriaL.

BACKGROUND: Elevated postprandial triglycerides reflect a proatherogenic milieu, but underlying mechanisms are unclear. OBJECTIVE: We examined differences between fasting and nonfasting profiles of directly measured lipoprotein size and subfractions to assess if postprandial triglycerides reflected increases in very low density lipoprotein (VLDL), intermediate density lipoprotein (IDL) and remnants, or small dense lipid depleted LDL (sdLDL) particles. METHODS: We conducted a cross-sectional analysis of 15,397 participants (10,135 fasting; 5262 nonfasting [<8 hours since last meal]) from the VITamin D and OmegA-3 TriaL. Baseline cholesterol subfractions were measured by the vertical auto profile method and particle subfractions by ion mobility. We performed multivariable linear regression adjusting for cardiovascular and lipoprotein-modifying risk factors. RESULTS: Mean age (SD) was 68.0 years (±7.0), with 50.9% women. Adjusted mean triglyceride concentrations were higher nonfasting by 17.8 ± 1.3%, with higher nonfasting levels of directly measured VLDL cholesterol (by 3.5 ± 0.6%) and total VLDL particles (by 2.0 ± 0.7%), specifically large VLDL (by 12.3 ± 1.3%) and medium VLDL particles (by 5.3 ± 0.8%), all P < .001. By contrast, lower concentrations of low density lipoprotein (LDL) and IDL cholesterol and particles were noted for nonfasting participants. sdLDL cholesterol levels and particle concentrations showed no statistically significant difference by fasting status (-1.3 ± 2.1% and 0.07 ± 0.6%, respectively, P > .05). CONCLUSIONS: Directly measured particle and cholesterol concentrations of VLDL, not sdLDL, were higher nonfasting and may partly contribute to the proatherogenicity of postprandial hypertriglyceridemia. These differences, although statistically significant, were small and may not fully explain the increased risk of postprandial hypertriglyceridemia.

Serum 25-hydroxyvitamin D in the VITamin D and OmegA-3 TriaL (VITAL): Clinical and demographic characteristics associated with baseline and change with randomized vitamin D treatment.

BACKGROUND: The VITamin D and OmegA-3 TriaL (VITAL) is a completed randomized, placebo-controlled trial of vitamin D3 (2000 IU/day) and marine omega-3 (1 g/day) supplements in the primary prevention of cancer and cardiovascular disease. Here we examine baseline and change in 25-hydroxyvitamin D (25(OH)D) and related biomarkers with randomized treatment and by clinical factors. METHODS: Baseline 25(OH)D was measured in 15,804 participants (mean age 68 years.; 50.8% women; 15.7% African Americans) and in 1660 1-year follow-up samples using liquid chromatography-tandem mass spectrometry and chemiluminescence. Calcium and parathyroid hormone (iPTH) were measured by chemiluminescence and spectrophotometry respectively. RESULTS: Mean baseline total 25(OH)D (ng/mL ±â€¯SD) was 30.8 ±â€¯10.0 ng/mL, and correlated inversely with iPTH (r = -0.28), p < .001. After adjusting for clinical factors, 25(OH)D (ng/mL ±â€¯SE) was lower in men vs women (29.7 ±â€¯0.30 vs 31.4 ±â€¯0.30, p < .0001) and in African Americans vs whites (27.9 ±â€¯0.29 vs 32.5 ±â€¯0.22, p < .0001). It was also lower with increasing BMI, smoking, and latitude, and varied by season. Mean 1-year 25(OH)D increased by 11.9 ng/mL in the active group and decreased by 0.7 ng/mL in placebo. The largest increases were noted among individuals with low baseline and African Americans. Results were similar for chemiluminescent immunoassay. Mean calcium was unchanged, and iPTH decreased with treatment. CONCLUSION: In VITAL, baseline 25(OH)D varied by clinical subgroups, was lower in men and African Americans. Concentrations increased with vitamin D supplementation, with the greatest increases in those with lower baseline 25(OH)D. The seasonal trends in 25(OH)D, iPTH, and calcium may be relevant when interpreting 25(OH)D levels for clinical treatment decisions. CLINICAL TRIAL REGISTRATION: VITAL ClinicalTrials.gov number NCT01169259.

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.